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Duration 14 hours
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI.
- Distinguishing between the two paradigms and exploring their complementary nature.
- Key use cases and industry trends.
Generative AI Architecture and Tools
- An overview of Transformer models, including GPT, LLaMA, and Claude.
- Analyzing the differences between fine-tuning and in-context learning.
- Essential tools: ChatGPT, Hugging Face Transformers, and Google AI Studio.
Prompt Engineering for Control and Structure
- Developing prompt patterns for writing, coding, and summarization.
- Techniques including few-shot, zero-shot, and chain-of-thought prompting.
- Utilizing prompt libraries and testing utilities.
Understanding Agentic AI
- Defining Agentic AI and tracing its evolution.
- Core architectures: planning, memory, tool usage, and self-reflection.
- Popular frameworks such as AutoGPT, BabyAGI, CrewAI, and LangGraph.
Designing and Deploying Autonomous Agents
- Strategies for goal setting and task decomposition.
- Integrating external tools and APIs, including search, memory, and code execution.
- Coordinating multi-agent systems and implementing human-in-the-loop supervision.
Use Cases and Implementation Scenarios
- Contrasting content generation with task orchestration.
- Applications in enterprise productivity, customer support, and data extraction.
- Ensuring responsible and secure implementation practices.
Summary and Next Steps
Requirements
- A foundational understanding of AI and machine learning principles.
- Hands-on experience with APIs or scripting languages, such as Python.
- Basic familiarity with prompt engineering or the utilization of large language models.
Target Audience
- AI developers and engineers.
- Innovation and Research & Development (R&D) teams.
- Technical product managers investigating agentic AI systems.
Testimonials (1)
the tips and recommended prompts that we can take away from this training
